KDDE - Knowledge Discovery and Data Engineering

11/09/2026

KDDE at ECMLPKDD 2026

The KDDE - Knowledge Discovery and Data Engineering research group took part in ECML PKDD 2026, the leading European conference on machine learning and data mining, held in Naples.

The first photo shows the KDDE members attending the conference. The second captures our extended research community, including colleagues now working at research institutions in Slovenia and the United States who have spent research periods at KDDE or completed part of their research training within our group.

It was a pleasure to meet again and to see how the scientific and personal connections developed at KDDE continue to grow across institutions and countries.

Donato Malerba Michelangelo Ceci Giusy Andresini Vincenzo Pasquadibisceglie Anthony Pellicani Roberto Hydergine Roberto Corizzo Dragi Kocev Aleksandra Rashkovska

09/09/2026

Today at 𝗘𝗖𝗠𝗟 𝗣𝗞𝗗𝗗 𝟮𝟬𝟮𝟲 in Naples, Anthony Pellicani presented the paper

“𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗶𝗻𝘀𝘁𝗮𝗻𝗰𝗲 𝘄𝗲𝗶𝗴𝗵𝘁𝗶𝗻𝗴 𝗳𝗼𝗿 𝗼𝗻𝗹𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗻 𝗺𝘂𝗹𝘁𝗶-𝗰𝗿𝘆𝗽𝘁𝗼𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗽𝗿𝗶𝗰𝗲 𝗮𝗻𝗱 𝘁𝗿𝗲𝗻𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴”,

co-authored with Gianvito Pio, Sašo Džeroski, and Michelangelo Ceci.

The work addresses online price and trend forecasting across multiple cryptocurrencies through 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗶𝗻𝘀𝘁𝗮𝗻𝗰𝗲 𝘄𝗲𝗶𝗴𝗵𝘁𝗶𝗻𝗴, with the aim of adapting learning to evolving data over time.

Another contribution from the KDDE - Knowledge Discovery and Data Engineering research group to the international research community on machine learning and knowledge discovery.

08/09/2026

Al workshop 𝗡𝗲𝘄 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿𝘀 𝗶𝗻 𝗠𝗶𝗻𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗗𝗮𝘁𝗮 (𝗡𝗙𝗠𝗖𝗗), nell’ambito di 𝗘𝗖𝗠𝗟 𝗣𝗞𝗗𝗗 𝟮𝟬𝟮𝟲 a Napoli, Vincenzo Pasquadibisceglie ha presentato il lavoro:

𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝗢𝘂𝘁𝗰𝗼𝗺𝗲-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗶𝗻 𝗛𝗮𝗱𝗼𝗼𝗽 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗙𝗶𝗹𝗲 𝗦𝘆𝘀𝘁𝗲𝗺𝘀

La ricerca affronta il tema del 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 orientato agli outcome, proponendo soluzioni capaci non soltanto di prevedere l’evoluzione futura dei processi, ma anche di migliorarne gli esiti e supportare tempestivamente le decisioni operative.

Il lavoro è stato sviluppato nell’ambito del progetto 𝗥𝗘𝗣𝗔 – 𝗮𝗥𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗶𝗻𝘁𝗘𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, finanziato dal PRIN, in collaborazione con Giusy Andresini, Donato Cancellara, Annalisa Appice e Donato Malerba.

Complimenti a Vincenzo per l’ottima presentazione e per il contributo alla discussione sulle nuove frontiere dell’analisi di dati e processi complessi!

11/08/2026

📢 𝗡𝗲𝘄 𝗞𝗗𝗗𝗘 𝗽𝗮𝗽𝗲𝗿 𝗻𝗼𝘄 𝗼𝗻𝗹𝗶𝗻𝗲!

Can adversarial malware fool not only a detector, but also the explanation of its decision?

We are pleased to share our new paper:

𝗔𝗱𝘃𝗲𝗿𝘀𝗮𝗿𝗶𝗮𝗹 𝗠𝗮𝗹𝘄𝗮𝗿𝗲 𝗖𝗮𝗻 𝗕𝗲 𝗕𝗼𝘁𝗵 𝗘𝘃𝗮𝘀𝗶𝘃𝗲 𝗮𝗻𝗱 𝗗𝗲𝗰𝗲𝗶𝘃𝗶𝗻𝗴: 𝗮 𝗚𝗿𝗮𝗱𝗶𝗲𝗻𝘁-𝗯𝗮𝘀𝗲𝗱 𝗔𝘁𝘁𝗮𝗰𝗸 𝗔𝗴𝗮𝗶𝗻𝘀𝘁 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻 𝗪𝗶𝗻𝗱𝗼𝘄𝘀 𝗣𝗘 𝗠𝗮𝗹𝘄𝗮𝗿𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻

by Luca Lobascio, Giuseppina Andresini, Annalisa Appice, and Donato Malerba.

The paper introduces 𝗚𝗔𝗠𝗘𝟰𝗘𝗫𝗘, a gradient-based adversarial framework designed to pursue two goals at the same time: making Windows PE malware evade deep neural malware detectors and manipulating the corresponding explanations so that they resemble those of benign software.

This dual perspective — attacking both 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 and 𝗲𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻 — raises an important challenge for trustworthy AI in cybersecurity: securing model decisions may not be enough if the explanations used to understand those decisions can also be deceived.

The preliminary evaluation investigates GAME4EXE against two deep learning malware detectors, MalConv and BBDNN, exploring classification evasion, transferability, and the ability to produce goodware-like explanations.

📍 2026 IEEE 11th European Symposium on Security and Privacy Workshops (EuroS&PW)
🔗 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗽𝗮𝗽𝗲𝗿 𝗼𝗻 𝗜𝗘𝗘𝗘 𝗫𝗽𝗹𝗼𝗿𝗲
DOI: 10.1109/EuroSPW72509.2026.00038

Research supported by FAIR – Future AI Research, Spoke 6 – Symbiotic AI, and SERICS under the NRRP MUR programme funded by the European Union – NextGenerationEU.

03/08/2026

📢 𝐍𝐞𝐰 𝐎𝐩𝐞𝐧 𝐀𝐜𝐜𝐞𝐬𝐬 𝐩𝐮𝐛𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐊𝐃𝐃𝐄 𝐋𝐚𝐛!

We are pleased to announce the publication of:

𝐂𝐈𝐂𝐄𝐑𝐎𝐍𝐄: 𝐀 𝐧𝐚𝐭𝐮𝐫𝐚𝐥 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞-𝐛𝐚𝐬𝐞𝐝 𝐠𝐥𝐨𝐛𝐚𝐥 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐟𝐨𝐫 𝐨𝐛𝐣𝐞𝐜𝐭-𝐜𝐞𝐧𝐭𝐫𝐢𝐜 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠

by Vincenzo Pasquadibisceglie, 𝐀𝐧𝐧𝐚𝐥𝐢𝐬𝐚 𝐀𝐩𝐩𝐢𝐜𝐞, and Donato Malerba.

CICERONE introduces an original approach to 𝐨𝐛𝐣𝐞𝐜𝐭-𝐜𝐞𝐧𝐭𝐫𝐢𝐜 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠, representing ongoing process executions as natural-language narratives that preserve the relationships among multiple interacting objects.

By combining these representations with a 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 and a 𝐠𝐥𝐨𝐛𝐚𝐥 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲, CICERONE can simultaneously generate predictions for all the objects involved in ongoing process executions.

Experiments on benchmark Object-Centric Event Logs demonstrate its effectiveness, while an occlusion-based analysis helps explain the impact of object interactions on the predictions.

🔗 Link to the paper in the first comment below!

25/07/2026

We are pleased to announce that the KDDE research group contributed three papers to the 27th International Symposium on Methodologies for Intelligent Systems — ISMIS 2026.

The papers explore different research challenges in predictive process monitoring, explainable and prescriptive process analytics, online deep learning, time-series forecasting and additive manufacturing.

🔹 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲 𝗟𝗲𝗻𝘀 𝗼𝗳 𝗗𝗲𝗲𝗽 𝗢𝗻𝗹𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
Vincenzo Pasquadibisceglie, Simone Capone, Annalisa Appice and Donato Malerba

The paper presents ATLAS, an online learning approach that continuously updates a deep predictive process monitoring model to address changes occurring in evolving business processes.

🔗 https://link.springer.com/chapter/10.1007/978-3-032-32643-0_20

🔹 𝗦𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆-𝗚𝘂𝗶𝗱𝗲𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗼𝗳 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 𝗳𝗿𝗼𝗺 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗼𝗳 𝗔𝗱𝗱𝗶𝘁𝗶𝘃𝗲𝗹𝘆 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗲𝗱 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀
Anthony Pellicani, Gianvito Pio, Donato Malerba and Michelangelo Ceci

The study introduces a similarity-guided method combining temporal clustering and LSTM-based models to improve multivariate forecasting from heterogeneous mechanical testing data.

🔗 https://link.springer.com/chapter/10.1007/978-3-032-32643-0_18

🏆 🔹 𝗖𝗼𝘂𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝘁𝘂𝗮𝗹𝘀 𝘁𝗼 𝗠𝗮𝗻𝗮𝗴𝗲 𝗢𝗻𝗴𝗼𝗶𝗻𝗴 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗗𝗲𝘃𝗶𝗮𝗻𝗰𝗲𝘀
Vincenzo Pasquadibisceglie, Rossella Anna Giansante, Annalisa Appice and Donato Malerba

The paper presents FIREFOX, a methodology that detects potential deviations in ongoing process executions and generates counterfactual recommendations for actions that may prevent undesirable outcomes.

We are especially proud that this contribution received the 𝗜𝗦𝗠𝗜𝗦 𝟮𝟬𝟮𝟲 𝗕𝗲𝘀𝘁 𝗣𝗮𝗽𝗲𝗿 𝗔𝘄𝗮𝗿𝗱. 🏆

🔗 https://link.springer.com/chapter/10.1007/978-3-032-32643-0_12

Congratulations to all the authors on this important collective achievement, which reflects the breadth and quality of the research conducted within the KDDE group!

24/07/2026

🏆 𝗕𝗲𝘀𝘁 𝗣𝗮𝗽𝗲𝗿 𝗔𝘄𝗮𝗿𝗱 𝗮𝘁 𝗜𝗦𝗠𝗜𝗦 𝟮𝟬𝟮𝟲!

We are delighted to announce that the paper

📄 𝗖𝗼𝘂𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝘁𝘂𝗮𝗹𝘀 𝘁𝗼 𝗠𝗮𝗻𝗮𝗴𝗲 𝗢𝗻𝗴𝗼𝗶𝗻𝗴 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗗𝗲𝘃𝗶𝗮𝗻𝗰𝗲𝘀

by 𝗩𝗶𝗻𝗰𝗲𝗻𝘇𝗼 𝗣𝗮𝘀𝗾𝘂𝗮𝗱𝗶𝗯𝗶𝘀𝗰𝗲𝗴𝗹𝗶𝗲, 𝗥𝗼𝘀𝘀𝗲𝗹𝗹𝗮 𝗔𝗻𝗻𝗮 𝗚𝗶𝗮𝗻𝘀𝗮𝗻𝘁𝗲, 𝗔𝗻𝗻𝗮𝗹𝗶𝘀𝗮 𝗔𝗽𝗽𝗶𝗰𝗲, and 𝗗𝗼𝗻𝗮𝘁𝗼 𝗠𝗮𝗹𝗲𝗿𝗯𝗮

has received the 𝗕𝗲𝘀𝘁 𝗣𝗮𝗽𝗲𝗿 𝗔𝘄𝗮𝗿𝗱 at the 28th International Symposium on Methodologies for Intelligent Systems — 𝗜𝗦𝗠𝗜𝗦 𝟮𝟬𝟮𝟲, held in Lyon, France.

The paper investigates how counterfactual explanations can support the management of ongoing business process deviations by suggesting actionable changes that may help steer a process towards a desired outcome.

This prestigious recognition rewards the research carried out within the KDDE - Knowledge Discovery and Data Engineering research group at the DIB - Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro.

We are grateful to the ISMIS 2026 organizers and Program Committee for this important recognition and proud to share this achievement with the entire 𝗞𝗗𝗗𝗘 team.

🔗 https://link.springer.com/chapter/10.1007/978-3-032-32643-0_12

25/06/2026

KDDE has organized a seminar for next month.

23/06/2026

🚀 New publication in Data Mining and Knowledge Discovery

We are pleased to announce the publication of the article:

“Dynamic instance weighting for online learning in multi-cryptocurrency price and trend forecasting”

authored by Anthony Pellicani, Gianvito Pio, Sašo Džeroski, and Michelangelo Ceci.

The paper introduces LEMON, a novel online learning approach for real-time forecasting of cryptocurrency price variations and market trends. LEMON leverages temporal correlations among cryptocurrencies, dynamically identifies groups with similar trends, and adopts an adaptive instance-weighting strategy to better model abrupt changes in streaming data.

Experiments on 16 cryptocurrency datasets show that LEMON outperforms state-of-the-art methods in both regression and classification tasks, supporting more accurate real-time predictions in highly volatile markets.

The work is the result of an international collaboration between researchers from the KDDE - Knowledge Discovery and Data Engineering Research Group, Università degli Studi di Bari Aldo Moro , and Sašo Džeroski from the Jožef Stefan Institute.

📖 Read the open access article here:
https://link.springer.com/article/10.1007/s10618-026-01232-9



Springer Nature

The cryptocurrency market represents a significant innovation in the financial ecosystem, built upon cryptographic principles to ensure secure and transparent transactions. Cryptocurrencies experienced a global adoption, driven by their decentralized nature that enables borderless transactions witho...

08/06/2026

We are proud to share the outstanding results achieved by the KDDE - Knowledge Discovery and Data Engineering research group in the latest Italian Research Quality Assessment (VQR).

Although the group was expected to contribute 22.5 research outputs, KDDE submitted 31 outputs, reflecting the strength and productivity of its research activities.

The evaluation results were exceptional: 20 outputs were rated Outstanding and 11 Excellent, meaning that 100% of the submitted research was placed in the two highest quality categories, with almost two-thirds achieving the highest rating.

This achievement is a testament to the scientific excellence, impact, and dedication of all KDDE researchers and collaborators.

Congratulations to everyone who contributed to this remarkable success!